In seismic data processing, waveform inversion may be employed to determine a seismic model of a subterranean formation based on the characteristics of the seismic signals. One type of inversion is Full Waveform Inversion (FWI), which is based on full wave propagation. FWI may be used for refining detailed seismic velocity fields, which may then benefit migration techniques to achieve enhanced subsurface images.
Some algorithms employ FWI iteratively to update the subsurface earth models and reduce the misfit function. The misfit function generally measures the difference between the recorded seismic data and the simulated waveforms, such that the full waveform (primary, multiples, converted wave etc.) of acquired seismic data may be explained by the inverted subsurface earth models.
FWI misfit analysis may employ a mean-square difference between the observed/acquired data and simulated/calculated data. However, it may be challenging to find the minimum misfit, because one or more local minima may be present, which may not correspond to the global minimum. This may make adjusting the model to address the differences between the data challenging. Further, misfit analysis may be complicated by cycle-skipping between the predicted and observed data. One reason for such cycle-skipping is the observed data may lack low-frequency information because of physical realities in data acquisition and noise in the recorded seismic signal. To address this challenge, the quality of the starting or initial model in the vicinity of global minimum may be relevant. In another words, the background model or the low wave-number components of the model may need to be sufficiently accurate, a priori, in order to start FWI.